Faculty Articles

Adaptive Control of a CSTR with a Neural Network Model

T. Knapp
H. Budman

Document Type

Article

Publication Title

Tappi Journal

ISSN

0959-1524

Publication Date

1-1-2001

Abstract

An adaptive control algorithm with a neural network model, previously proposed in the literature for the control of mechanical manipulators, is applied to a CSTR (Continuous Stirred Tank Reactor). The neural network model uses either radial Gaussian or “Mexican hat” wavelets as basis functions. This work shows that the addition of linear functions to the networks significantly improves the error convergence when the CSTR is operated for long periods of time in a neighborhood of one operating point, a common scenario in chemical process control. Then, a quantitative comparative study based on output errors and control efforts is conducted where adaptive controllers using wavelets or Gaussian basis functions and PID controllers (IMC tuning with fixed parameters and self tuning PID) are compared. From this comparative study, the practicality and advantages of the adaptive controllers over fixed or adaptive PID control is assessed.

DOI

10.1016/S0959-1524(99)00065-7

Volume

11

First Page

53

Last Page

68

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